Financing and provision of healthcare for two billion people in low-income nations: Is the cooperative healthcare model a solution?

The international consensus in support of universal health coverage (UHC), though commendable, thus far lacks a clear mechanism to finance and deliver accessible and effective basic healthcare to the two billion rural residents and informal workers of low- and lower-middle-income countries (LLMICs). Importantly, the two preferred financing modes for UHC, general tax revenue and social health insurance, are often infeasible for LLMICs. We identify from historical examples a community-based model that we argue shows promise as a solution to this problem. This model, which we call Cooperative Healthcare (CH), is characterized by community-based risk-pooling and governance and prioritizes primary care. CH leverages communities' existing social capital, such that even those for whom the private benefit of enrolling in a CH scheme is outweighed by the cost may choose to enroll (given sufficient social capital). For CH to be scalable, it needs to demonstrate that it can organize delivery of accessible and reasonable-quality primary healthcare that people value, with management accountable to the communities themselves through structures that people trust, combined with government legitimacy. Once LLMICs with CH programs have industrialized sufficiently to make universal social health insurance feasible, CH schemes can be rolled into such universal programs. We defend cooperative healthcare's suitability for this bridging role and urge LLMIC governments to launch experiments testing it out, with careful adaptation to local conditions.

OntoTrek: 3D visualization of application ontology class hierarchies.

An application ontology often reuses terms from other related, compatible ontologies. The extent of this interconnectedness is not readily apparent when browsing through larger textual presentations of term class hierarchies, be it Manchester text format OWL files or within an ontology editor like Protege. Users must either note ontology sources in term identifiers, or look at ontology import file term origins. Diagrammatically, this same information may be easier to perceive in 2 dimensional network or hierarchical graphs that visually code ontology term origins. However, humans, having stereoscopic vision and navigational acuity around colored and textured shapes, should benefit even more from a coherent 3-dimensional interactive visualization of ontology that takes advantage of perspective to offer both foreground focus on content and a stable background context. We present OntoTrek, a 3D ontology visualizer that enables ontology stakeholders-students, software developers, curation teams, and funders-to recognize the presence of imported terms and their domains, ultimately illustrating how projects can capture knowledge through a vocabulary of interwoven community-supported ontology resources.

The DataHarmonizer: a tool for faster data harmonization, validation, aggregation and analysis of pathogen genomics contextual information.

Pathogen genomics is a critical tool for public health surveillance, infection control, outbreak investigations as well as research. In order to make use of pathogen genomics data, they must be interpreted using contextual data (metadata). Contextual data include sample metadata, laboratory methods, patient demographics, clinical outcomes and epidemiological information. However, the variability in how contextual information is captured by different authorities and how it is encoded in different databases poses challenges for data interpretation, integration and their use/re-use. The DataHarmonizer is a template-driven spreadsheet application for harmonizing, validating and transforming genomics contextual data into submission-ready formats for public or private repositories. The tool's web browser-based JavaScript environment enables validation and its offline functionality and local installation increases data security. The DataHarmonizer was developed to address the data sharing needs that arose during the COVID-19 pandemic, and was used by members of the Canadian COVID Genomics Network (CanCOGeN) to harmonize SARS-CoV-2 contextual data for national surveillance and for public repository submission. In order to support coordination of international surveillance efforts, we have partnered with the Public Health Alliance for Genomic Epidemiology to also provide a template conforming to its SARS-CoV-2 contextual data specification for use worldwide. Templates are also being developed for One Health and foodborne pathogens. Overall, the DataHarmonizer tool improves the effectiveness and fidelity of contextual data capture as well as its subsequent usability. Harmonization of contextual information across authorities, platforms and systems globally improves interoperability and reusability of data for concerted public health and research initiatives to fight the current pandemic and future public health emergencies. While initially developed for the COVID-19 pandemic, its expansion to other data management applications and pathogens is already underway.

Canadians' opinions towards COVID-19 data-sharing: a national cross-sectional survey.

OBJECTIVES: COVID-19 research has significantly contributed to pandemic response and the enhancement of public health capacity. COVID-19 data collected by provincial/territorial health authorities in Canada are valuable for research advancement yet not readily available to the public, including researchers. To inform developments in public health data-sharing in Canada, we explored Canadians' opinions of public health authorities sharing deidentified individual-level COVID-19 data publicly. DESIGN/SETTING/INTERVENTIONS/OUTCOMES: A national cross-sectional survey was administered in Canada in March 2022, assessing Canadians' opinions on publicly sharing COVID-19 datatypes. Market research firm Léger was employed for recruitment and data collection. PARTICIPANTS: Anyone greater than or equal to 18 years and currently living in Canada. RESULTS: 4981 participants completed the survey with a 92.3% response rate. 79.7% were supportive of provincial/territorial authorities publicly sharing deidentified COVID-19 data, while 20.3% were hesitant/averse/unsure. Datatypes most supported for being shared publicly were symptoms (83.0% in support), geographical region (82.6%) and COVID-19 vaccination status (81.7%). Datatypes with the most aversion were employment sector (27.4% averse), postal area (26.7%) and international travel history (19.7%). Generally supportive Canadians were characterised as being ≥50 years, with higher education, and being vaccinated against COVID-19 at least once. Vaccination status was the most influential predictor of data-sharing opinion, with respondents who were ever vaccinated being 4.20 times more likely (95% CI 3.21 to 5.48, p=0.000) to be generally supportive of data-sharing than those unvaccinated. CONCLUSIONS: These findings suggest that the Canadian public is generally favourable to deidentified data-sharing. Identifying factors that are likely to improve attitudes towards data-sharing are useful to stakeholders involved in data-sharing initiatives, such as public health agencies, in informing the development of public health communication and data-sharing policies. As Canada progresses through the COVID-19 pandemic, and with limited testing and reporting of COVID-19 data, it is essential to improve deidentified data-sharing given the public's general support for these efforts.

CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database.

The Comprehensive Antibiotic Resistance Database (CARD; card.mcmaster.ca) combines the Antibiotic Resistance Ontology (ARO) with curated AMR gene (ARG) sequences and resistance-conferring mutations to provide an informatics framework for annotation and interpretation of resistomes. As of version 3.2.4, CARD encompasses 6627 ontology terms, 5010 reference sequences, 1933 mutations, 3004 publications, and 5057 AMR detection models that can be used by the accompanying Resistance Gene Identifier (RGI) software to annotate genomic or metagenomic sequences. Focused curation enhancements since 2020 include expanded ß-lactamase curation, incorporation of likelihood-based AMR mutations for Mycobacterium tuberculosis, addition of disinfectants and antiseptics plus their associated ARGs, and systematic curation of resistance-modifying agents. This expanded curation includes 180 new AMR gene families, 15 new drug classes, 1 new resistance mechanism, and two new ontological relationships: evolutionary_variant_of and is_small_molecule_inhibitor. In silico prediction of resistomes and prevalence statistics of ARGs has been expanded to 377 pathogens, 21,079 chromosomes, 2,662 genomic islands, 41,828 plasmids and 155,606 whole-genome shotgun assemblies, resulting in collation of 322,710 unique ARG allele sequences. New features include the CARD:Live collection of community submitted isolate resistome data and the introduction of standardized 15 character CARD Short Names for ARGs to support machine learning efforts.

Comprehensive Assessment of Health System Performance in Odisha, India.

India has recently implemented several major health care reforms at national and state levels, yet the nation continues to face significant challenges in achieving better health system performance. These challenges are particularly daunting in India's poorer states, like Odisha. The first step toward overcoming these challenges is to understand their root causes. Toward this end, the Harvard T.H. Chan School of Public Health conducted a comprehensive study in Odisha based on ten new field surveys of the system's performance to provide a multi-perspective analysis. This article reports on the assessment of the performance of Odisha's health system and the preliminary diagnosis of underlying causes of the strengths and challenges. This comprehensive health system assessment is aimed toward the overarching goals of informing and supporting efforts to improve the performance of health systems in Odisha and other similar contexts.

ProbeTools: designing hybridization probes for targeted genomic sequencing of diverse and hypervariable viral taxa.

BACKGROUND: Sequencing viruses in many specimens is hindered by excessive background material from hosts, microbiota, and environmental organisms. Consequently, enrichment of target genomic material is necessary for practical high-throughput viral genome sequencing. Hybridization probes are widely used for enrichment in many fields, but their application to viral sequencing faces a major obstacle: it is difficult to design panels of probe oligo sequences that broadly target many viral taxa due to their rapid evolution, extensive diversity, and genetic hypervariability. To address this challenge, we created ProbeTools, a package of bioinformatic tools for generating effective viral capture panels, and for assessing coverage of target sequences by probe panel designs in silico. In this study, we validated ProbeTools by designing a panel of 3600 probes for subtyping the hypervariable haemagglutinin (HA) and neuraminidase (NA) genome segments of avian-origin influenza A viruses (AIVs). Using in silico assessment of AIV reference sequences and in vitro capture on egg-cultured viral isolates, we demonstrated effective performance by our custom AIV panel and ProbeTools' suitability for challenging viral probe design applications. RESULTS: Based on ProbeTool's in silico analysis, our panel provided broadly inclusive coverage of 14,772 HA and 11,967 NA reference sequences. For each reference sequence, we calculated the percentage of nucleotide positions covered by our panel in silico; 90% of HA and NA references sequences had at least 90.8 and 95.1% of their nucleotide positions covered respectively. We also observed effective in vitro capture on a representative collection of 23 egg-cultured AIVs that included isolates from wild birds, poultry, and humans and representatives from all HA and NA subtypes. Forty-two of forty-six HA and NA segments had over 98.3% of their nucleotide positions significantly enriched by our custom panel. These in vitro results were further used to validate ProbeTools' in silico coverage assessment algorithm; 89.2% of in silico predictions were concordant with in vitro results. CONCLUSIONS: ProbeTools generated an effective panel for subtyping AIVs that can be deployed for genomic surveillance, outbreak prevention, and pandemic preparedness. Effective probe design against hypervariable AIV targets also validated ProbeTools' design and coverage assessment algorithms, demonstrating their suitability for other challenging viral capture applications.

The metabolic potential of the paediatric-onset multiple sclerosis gut microbiome.

BACKGROUND: The aim of this study was to examine the gut microbiome's metabolic potential in paediatric-onset MS patients (symptom onset <18 years). METHODS: We included 17 MS participants and 20 controls similar for sex, age, race, and stool consistency from the Canadian Paediatric Demyelinating Disease Network study. Stool-derived gut metagenome gene abundances were used to estimate relative abundances and turnover scores of individual microbial metabolites and the composition and diversity of carbohydrate-active enzymes (CAZymes). MS participants and controls were compared using the Wilcoxon rank-sum test, as were the disease-modifying drug (DMD) exposed and naïve MS participants. RESULTS: The median age(s) at MS symptom onset=16.1 years (interquartile range [IQR]=1.7), and at stool sample procurement=16.9/15.8 years (IQR=2.0/1.4), for the MS participants/controls. Most MS and control participants were girls (80-82%). Five (29%) of the MS participants had never been exposed to a DMD pre-stool sample and 12 (71%) had (7 to beta-interferon and 5 glatiramer acetate). While the relative abundance of metabolites did not differ between MS participants and controls, turnover scores did. MS participants had a greater potential to metabolize lipopolysaccharides than controls (score difference=1.6E-04, p = 0.034) but lower potential to metabolize peptidoglycan molecules and starch (score differences<2.2E-02, p<0.040). Further, although CAZymes diversity did not differ (p>0.050), starch-degrading subfamilies were underrepresented in MS participants versus controls (relative abundance differences >-0.34, p<0.040) and in the DMD exposed verses DMD naïve MS participants (relative abundance differences>-0.20, p<0.049). CONCLUSION: Paediatric-onset MS participants had an altered gut microbiome-related metabolic potential compared to controls, including higher breakdown of lipopolysaccharide molecules, but lower resistant starch metabolism.

Future-proofing and maximizing the utility of metadata: The PHA4GE SARS-CoV-2 contextual data specification package.

BACKGROUND: The Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) is a global coalition that is actively working to establish consensus standards, document and share best practices, improve the availability of critical bioinformatics tools and resources, and advocate for greater openness, interoperability, accessibility, and reproducibility in public health microbial bioinformatics. In the face of the current pandemic, PHA4GE has identified a need for a fit-for-purpose, open-source SARS-CoV-2 contextual data standard. RESULTS: As such, we have developed a SARS-CoV-2 contextual data specification package based on harmonizable, publicly available community standards. The specification can be implemented via a collection template, as well as an array of protocols and tools to support both the harmonization and submission of sequence data and contextual information to public biorepositories. CONCLUSIONS: Well-structured, rich contextual data add value, promote reuse, and enable aggregation and integration of disparate datasets. Adoption of the proposed standard and practices will better enable interoperability between datasets and systems, improve the consistency and utility of generated data, and ultimately facilitate novel insights and discoveries in SARS-CoV-2 and COVID-19. The package is now supported by the NCBI's BioSample database.

Metagenomic Analysis of the Pediatric-Onset Multiple Sclerosis Gut Microbiome.

BACKGROUND AND OBJECTIVES: Little is known of the functional potential of the gut microbiome in pediatric-onset multiple sclerosis (MS). We performed metagenomic analyses using stool samples from individuals with pediatric-onset MS and unaffected controls. METHODS: Persons ≤21 years old enrolled in the Canadian Pediatric Demyelinating Disease Network providing a stool sample were eligible. Twenty patients with MS (McDonald criteria) with symptom onset <18 years were matched to 20 controls by sex, age (±3 years), stool consistency, and race. Microbial taxonomy and functional potentials were estimated from stool sample-derived metagenomic reads and compared by disease status (MS vs controls) and disease-modifying drug (DMD) exposure using alpha diversity, relative abundance, and prevalence using Wilcoxon rank sum, ALDEx2, and Fisher exact tests, respectively. RESULTS: Individuals with MS were aged 13.6 years (mean) at symptom onset and 8 were DMD-naive. Mean ages at stool sample were 16.1 and 15.4 years for MS and control participants, respectively; 80% were girls. Alpha diversity of enzymes and proteins did not differ by disease or DMD status (p > 0.20), but metabolic pathways, gene annotations, and microbial taxonomy did. Individuals with MS (vs controls) exhibited higher methanogenesis prevalence (odds ratio 10, p = 0.044) and Methanobrevibacter abundance (log2 fold change [LFC] 1.7, p = 0.0014), but lower homolactic fermentation abundance (LFC -0.48, p = 0.039). Differences by DMD status included lower phosphate butyryl transferase for DMD-naive vs exposed patients with MS (LFC -1.0, p = 0.033). DISCUSSION: The gut microbiome's functional potential and taxonomy differed between individuals with pediatric-onset MS vs controls, including higher prevalence of a methane-producing pathway from Archaea and depletion of the lactate fermentation pathway. DMD exposure was associated with butyrate-producing enzyme enrichment. Together these findings indicate that the gut microbiome of individuals with MS may have a disturbed functional potential.

Large-scale comparative genomics to refine the organization of the global Salmonella enterica population structure.

The White-Kauffmann-Le Minor (WKL) scheme is the most widely used Salmonella typing scheme for reporting the disease prevalence of the enteric pathogen. With the advent of whole-genome sequencing (WGS), in silico methods have increasingly replaced traditional serotyping due to reproducibility, speed and coverage. However, despite integrating genomic-based typing by in silico serotyping tools such as SISTR, in silico serotyping in certain contexts remains ambiguous and insufficiently informative. Specifically, in silico serotyping does not attempt to resolve polyphyly. Furthermore, in spite of the widespread acknowledgement of polyphyly from genomic studies, the prevalence of polyphyletic serovars is not well characterized. Here, we applied a genomics approach to acquire the necessary resolution to classify genetically discordant serovars and propose an alternative typing scheme that consistently reflect natural Salmonella populations. By accessing the unprecedented volume of bacterial genomic data publicly available in GenomeTrakr and PubMLST databases (>180 000 genomes representing 723 serovars), we characterized the global Salmonella population structure and systematically identified putative non-monophyletic serovars. The proportion of putative non-monophyletic serovars was estimated higher than previous reports, reinforcing the inability of antigenic determinants to depict the complexity of Salmonella evolutionary history. We explored the extent of genetic diversity masked by serotyping labels and found significant intra-serovar molecular differences across many clinically important serovars. To avoid false discovery due to incorrect in silico serotyping calls, we cross-referenced reported serovar labels and concluded a low error rate in in silico serotyping. The combined application of clustering statistics and genome-wide association methods demonstrated effective characterization of stable bacterial populations and explained functional differences. The collective methods adopted in our study have practical values in establishing genomic-based typing nomenclatures for an entire microbial species or closely related subpopulations. Ultimately, we foresee an improved typing scheme to be a hybrid that integrates both genomic and antigenic information such that the resolution from WGS is leveraged to improve the precision of subpopulation classification while preserving the common names defined by the WKL scheme.

A SYSTEMATIC REVIEW AND NARRATIVE SYNTHESIS OF THE USE OF ENVIRONMENTAL SAMPLES FOR THE SURVEILLANCE OF AVIAN INFLUENZA VIRUSES IN WILD WATERBIRDS.

Wild waterbirds are reservoir hosts for avian influenza viruses (AIV), which can cause devastating outbreaks in multiple species, making them a focus for surveillance efforts. Traditional AIV surveillance involves direct sampling of live or dead birds, but environmental substrates present an alternative sample for surveillance. Environmental sampling analyzes AIV excreted by waterbirds into the environment and complements direct bird sampling by minimizing financial, logistic, permitting, and spatial-temporal constraints associated with traditional surveillance. Our objectives were to synthesize the literature on environmental AIV surveillance, to compare and contrast the different sample types, and to identify key themes and recommendations to aid in the implementation of AIV surveillance using environmental samples. The four main environmental substrates for AIV surveillance are feces, feathers, water, and sediment or soil. Feces were the most common environmental substrate collected. The laboratory analysis of water and sediment provided challenges, such as low AIV concentration, heterogenous AIV distribution, or presence of PCR inhibitors. There are a number of abiotic and biotic environmental factors, including temperature, pH, salinity, or presence of filter feeders, that can influence the presence and persistence of AIV in environmental substrates; however, the nature of this influence is poorly understood in field settings, and field data from southern, coastal, and tropical ecosystems are underrepresented. Similarly, there are few studies comparing the performance of environmental samples to each other and to samples collected in wild waterbirds, and environmental surveillance workflows have yet to be validated or optimized. Environmental samples, particularly when used in combination with new technology such as environmental DNA and next generation sequencing, provided information on trends in AIV detection rates and circulating subtypes that complemented traditional, direct waterbird sampling. The use of environmental samples for AIV surveillance also shows significant promise for programs whose goal is early warning of high-risk subtypes.

Addressing Privacy Concerns in Sharing Viral Sequences and Minimum Contextual Data in a Public Repository During the COVID-19 Pandemic.

COVID-19 was declared to be a pandemic in March 2020 by the World Health Organization. Timely sharing of viral genomic sequencing data accompanied by a minimal set of contextual data is essential for informing regional, national, and international public health responses. Such contextual data is also necessary for developing, and improving clinical therapies and vaccines, and enhancing the scientific community's understanding of the SARS-CoV-2 virus. The Canadian COVID-19 Genomics Network (CanCOGeN) was launched in April 2020 to coordinate and upscale existing genomics-based COVID-19 research and surveillance efforts. CanCOGeN is performing large-scale sequencing of both the genomes of SARS-CoV-2 virus samples (VirusSeq) and affected Canadians (HostSeq). This paper addresses the privacy concerns associated with sharing the viral sequence data with a pre-defined set of contextual data describing the sample source and case attribute of the sequence data in the Canadian context. Currently, the viral genome sequences are shared by provincial public health laboratories and their healthcare and academic partners, with the Canadian National Microbiology Laboratory and with publicly accessible databases. However, data sharing delays and the provision of incomplete contextual data often occur because publicly releasing such data triggers privacy and data governance concerns. The CanCOGeN Ethics and Governance Expert Working Group thus has investigated several privacy issues cited by CanCOGeN data providers/stewards. This paper addresses these privacy concerns and offers insights primarily in the Canadian context, although similar privacy considerations also exist in other jurisdictions. We maintain that sharing viral sequencing data and its limited associated contextual data in the public domain generally does not pose insurmountable privacy challenges. However, privacy risks associated with reidentification should be actively monitored due to advancements in reidentification methods and the evolving pandemic landscape. We also argue that during a global health emergency such as COVID-19, privacy should not be used as a blanket measure to prevent such genomic data sharing due to the significant benefits it provides towards public health responses and ongoing research activities.

Extraction and Detection of Avian Influenza Virus From Wetland Sediment Using Enrichment-Based Targeted Resequencing.

Early virus detection and characterization is key to successful avian influenza virus (AIV) surveillance for the health of humans as well as domestic poultry. We explored a novel sampling approach and molecular strategy using sediment from wetlands and outdoor waterbodies on poultry farms as a population-level proxy of AIV activity in waterfowls. RNA was extracted using the MoBio RNA PowerSoil Total RNA isolation kit with additional chloroform extraction steps to reduce PCR inhibition. AIV matrix protein (MP) gene was detected in 42/345 (12.2%) samples by RT-qPCR; an additional 64 (18.6%) samples showed evidence of amplification below the threshold and were categorized as "suspect positive." Enrichment-based targeted resequencing (TR) identified AIV sequences in 79/345 (22.9%) samples. TR probes were designed for MP, hemagglutinin (HA), and neuraminidase (NA), however PB2 and PA were also identified. Although RT-qPCR and TR only had fair-moderate agreement, RT-qPCR positivity was predictive of TR-positivity both when using only strictly positive RT-qPCR samples (OR = 11.29) and when coding suspect positives as positive (OR = 7.56). This indicates that RT-qPCR could be used as a screening tool to select samples for virus characterization by TR and that future studies should consider RT-qPCR suspect positives to be positive samples for subsequent resequencing when avoiding false negatives is the priority, for instance in a diagnostic test, and to consider suspect positives to be negative samples when cost efficiency over a large number of samples is the priority, for instance in a surveillance program. A total of 13 HA (H1-7, H9-13, H16) and 9 NA (N1-9) subtypes were identified, with a maximum of 8 HA and 8 NA subtypes detected in a single sample. The optimized RNA extraction and targeted resequencing methods provided increased virus detection and subtyping characterization that could be implemented in an AIV surveillance system.

The Case Against Labor-Tax-Financed Social Health Insurance For Low- And Low-Middle-Income Countries.

An increasing interest in initiating and expanding social health insurance through labor taxes in low- and low-middle-income countries goes against available empirical evidence. This article builds on existing recommendations by leading health financing experts and summarizes recent research that makes the case against labor-tax financing of health care in low- and low-middle-income countries. We found very little evidence to justify the pursuit of labor-tax financing for health care in these countries and persistent evidence that such policies could lead to increased inequality and fragmentation of the health system. We recommend that countries considering such policies heed the evidence on labor-tax financing and seek alternative approaches to health financing: primarily using general taxes or, depending on the context, general taxes combined with adequately regulated insurance premiums.

Influenza Classification Suite: An automated Galaxy workflow for rapid influenza sequence analysis.

Influenza viruses continually evolve to evade population immunity, and the different lineages are assigned into clades based on shared mutations. We have developed a publicly available computational workflow, the Influenza Classification Suite, for rapid clade mapping of sequenced influenza viruses. This suite provides a user-friendly workflow implemented in Galaxy to automate clade calling and antigenic site extraction. Workflow input includes clade definition and amino acid index array files, which can be customized to identify any clades of interest. The Influenza Classification Suite provides rapid, high-resolution understanding of circulating influenza strain evolution to inform influenza vaccine effectiveness and the need for potential vaccine reformulation.

CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database.

The Comprehensive Antibiotic Resistance Database (CARD; https://card.mcmaster.ca) is a curated resource providing reference DNA and protein sequences, detection models and bioinformatics tools on the molecular basis of bacterial antimicrobial resistance (AMR). CARD focuses on providing high-quality reference data and molecular sequences within a controlled vocabulary, the Antibiotic Resistance Ontology (ARO), designed by the CARD biocuration team to integrate with software development efforts for resistome analysis and prediction, such as CARD's Resistance Gene Identifier (RGI) software. Since 2017, CARD has expanded through extensive curation of reference sequences, revision of the ontological structure, curation of over 500 new AMR detection models, development of a new classification paradigm and expansion of analytical tools. Most notably, a new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes. By adding these resistance variants to CARD, we are able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants. Here, we describe updates and recent expansions to CARD and its biocuration process, including new resources for community biocuration of AMR molecular reference data.

TARGETED RESEQUENCING OF WETLAND SEDIMENT AS A TOOL FOR AVIAN INFLUENZA VIRUS SURVEILLANCE.

Surveillance methods for avian influenza virus (AIV) based upon collecting and testing samples from individual wild birds have several significant limitations primarily related to the difficulties associated with obtaining samples. Because AIVs are shed in waterfowl feces, the use of environmental substrates where waterfowl feces accumulate may overcome some of these limitations. However, these substrates are difficult to analyze using traditional diagnostic techniques, such as virus culture and PCR, because of virus inactivation, RNA degradation, low concentration of target RNA, microbial complexity, presence of inhibitory substances, and other factors. We investigated the use of a genomics-based approach called targeted resequencing to detect and characterize AIVs in wetland sediments during the 2014-15 North American highly pathogenic avian influenza outbreak. We identified AIV in 20.6% (71/345) sediment samples obtained from wetlands (n=15) and outdoor waterbodies on AIV-infected poultry farms (n=10) in British Columbia, Canada (the first area affected during the outbreak). Thirteen hemagglutinin (HA) and nine neuraminidase (NA) subtypes were detected, including H5, N1, and N8 sequences that clustered with other sequences associated with the North American outbreak. Additionally, as many as eight HA and eight NA subtypes could be detected in a single sediment sample. This proof-of-concept study shows the potential utility of sediment sampling coupled with genomics-based analysis as a tool for AIV surveillance.